This study aims to develop and validate a user experience scale to construct an Actual System Use model for online courses based on the Technology Acceptance Model, allowing for a comprehensive assessment of the multidimensional factors affecting Learning Outcomes and Actual System Use in the context of online courses. The scale includes six core dimensions: Interactive Experience, Content Quality, Learning Outcomes, Teaching Quality, Technical Support, and Learning Motivation. Through a literature review, pre-survey, exploratory factor analysis, and confirmatory factor analysis, the reliability and validity of the developed scale were verified. A second-order complex Structural Equation Model was used to measure users’ Actual System Use with respect to online courses. The results demonstrate that the Interactive Experience and Learning Motivation dimensions play crucial roles in enhancing learners’ engagement and learning satisfaction, while Perceived Usefulness and Perceived Ease of Use significantly influence system usage behaviors. This study provides a systematic theoretical basis and empirical data for the design of online courses, offering valuable insights for optimizing course design and enhancing user experiences.

This research work deals with the potential evidence to influence ICT towards the agricultural sector. Indian agriculture is gradually languishing due to the lack of technological inputs and advancement. Information with use of ICT for farmers should be gathered and processed properly by networking and ICT. The optimum utilization of ICT will be utilized for the farmers which paved the way for upheavals in agriculture development and our farmers too. E-governance is one of the ways to reach all farmers to promote availability of resources through information and communication technology, and it is a tool to promote sustainable agriculture as well. In India, lots of e-governance schemes and programmes are available for agricultural sustainability.

Purpose: The study's goal is to better understand how older and younger generations in Phnom Penh, Cambodia, behave when it comes to using digital payments. Investigating the choice of mobile payment application in the Phnom Penh region is another objective of this study. Here, it is also investigated how usage, economic situation, and adoption of mobile payments are related. Theoretical framework: One of the most well-known research models on consumer behavior among younger and older generations and the adoption of new technical systems emphasizes the influence of geography, socioeconomic status, and specific traits on consumer acceptance. The concept considers factors including social effects, credibility requirements, effort expectations, and performance requirements when determining the best digital payment application. Design/Methodology/Approach: The Unified Theory of Acceptance and Use of Technology (UTAUT) model and K-mean clustering for distance and economic status studies were used in this research paper to select 150 locals in Phnom Penh from the target population in order to study consumer behavioral intention to accept digital payment method and application. Findings: The result we aimed to show and analyses the Digital payment methods, payments sharing, receiving, storing evidences, that older generation started understanding the digital payment by Khmer language and accepting the digital transactions from known customers. The current generation using Sathapana application and English Language in wide ranges through the Phom Penh region. The study also trying to exhibit the purpose of the digital payment systems in and across the e-commerce activities, through the older and younger generation. Research, Practical & Social implications: The elder generation Z handled digital payments for regional transactions like banking, home tasks, loan repayment, peer lending, travel, and local marketplaces in Khmer. The subsequent generations of millennials also incorporated cross-border payments in English and Khmer. Originality/Value: Bringing up a Digital Platform shall make a positive change in economy and circulation of economy to the people and country. Unless the people trust the digital payments, they cant share their money for the business or basic operations to run the government. Thy the Digital Payment applications shall understand the behavior of the customers and share the services through the digital payment systems.

The subject of this article is the use of Digital Human Technology (DHT) in live broadcasts on social media platforms and its impact on audience engagement and content appeal. This study examines how elements of DHT, such as virtual avatars and AI-driven hosts, are increasingly being used in live broadcasts to improve viewer engagement and retention. The main objective is to evaluate whether the integration of DHT increases viewer engagement, interactivity, and retention, especially compared to traditional streaming methods. This study consists of several key tasks: reviewing the current live DHT landscape, developing a research framework for analyzing engagement metrics, collecting empirical data through surveys and interviews and conducting statistical analysis to identify correlations between DHT use and viewer engagement. The methods used in this study include quantitative approaches such as structured questionnaires to measure viewership indicators and qualitative approaches such as in-depth interviews with streamers and viewers. Statistical methods, such as factor and correlation analysis, are used to assess the impact of DGT on key engagement metrics, such as viewing time, frequency of interaction, and viewer satisfaction. Through systematic observation, this study also captured real-time interactions, providing a comprehensive understanding of DHT effects. The findings emphasize that DHT significantly increases engagement in live broadcasts, providing content creators with innovative ways to retain audiences. However, the high cost of such technology and technical requirements limit its availability to independent streamers. This study provides practical recommendations for streamers and marketers that suggest DHT is a valuable tool for optimizing content appeal and audience engagement. Future research should explore scalable DHT solutions to improve accessibility for a wider range of content creators.

Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing factors and their mechanisms vary significantly across regions, particularly along the Chengdu–Lhasa (G318) route, which features large elevation changes, diverse climatic conditions, rugged terrain, and frequent geological disasters, making the influencing factors particularly complex. This study analyzes comment texts from 38 shared charging stations along the G318 route in the e-Charging APP, totaling 15,214 comments. A comprehensive approach is employed, including high-frequency word analysis, term frequency–inverse document frequency (TF-IDF) comparison, co-occurrence semantic network and co-word matrix feature correlation analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis. This multifaceted analysis explores core themes, user viewpoints, and sentiments in the comments, focusing on users’ perspectives on service quality, usage experience, and environmental impact of the charging stations. The findings indicate that charging speed, service attitude, environment, operational status of hardware and software, and pricing are key factors influencing user sentiment. Users have a high demand for the perfection of supporting facilities of shared charging stations, directly affecting user satisfaction and indirectly influencing the brand image and market competitiveness of enterprises.

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.